Advanced Topics in Statistical Machine Learning - COMP9418

Faculty: Faculty of Engineering

School: School of Computer Science and Engineering

Course Outline:

Campus: Sydney

Career: Undergraduate

Units of Credit: 6

EFTSL: 0.12500 (more info)

Indicative Contact Hours per Week: 4

Enrolment Requirements:

Prerequisite: COMP9417.

CSS Contribution Charge: 2 (more info)

Tuition Fee: See Tuition Fee Schedule

Further Information: See Class Timetable

View course information for previous years.


This course provides an in-depth study of statistical machine learning approaches. The focus will be on methods for learning and inference in structured probabilistic models, with a healthy balance of theory and practice. It is aimed at postgraduate students and advanced undergraduates who are willing to go beyond basic understanding of machine learning.

The course provides fundamental support for those willing to intensify their knowledge in the area of big data analytics. It will cover topics on exact and approximate inference in probabilistic graphical models, learning in structured latent variable models, and posterior inference in non-parametric models based on Gaussian processes.
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